Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking

BACKGROUND

Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and "omics" data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research.

METHODS

In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting "Big Data" and the analytic approaches used for analysis.

RESULTS

These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies.

CONCLUSIONS

The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Biobanks are key research infrastructures aimed at the collection, storage, processing, and sharing of high-quality human biological samples and associated data for research, diagnosis, and personalized medicine. The Biobank for Translational and Digital Medicine Unit at the European Institute of Oncology (IEO) is a landmark in this field. Biobanks collaborate with clinical divisions, internal and external research groups, and industry, supporting patients' treatment and scientific progress, including innovative diagnostics, biomarker discovery, and clinical trial design. Given the central role of biobanks in modern research, biobanking standard operating procedures (SOPs) should be extremely precise. SOPs and controls by certified specialists ensure the highest quality of samples for the implementation of science-based, diagnostic, prognostic, and therapeutic personalized strategies. However, despite numerous efforts to standardize and harmonize biobanks, these protocols, which follow a strict set of rules, quality controls, and guidelines based on ethical and legal principles, are not easily accessible. This paper presents the biobank standard operating procedures of a large cancer center.

Standard operating procedures for biobank in oncology

Biobanks are biorepositories that collect, process, store, catalog, and distribute human biological samples, and record the associated data. The role and action field of these strategic infrastructures for implementing precision medicine in translational research is continuously evolving. To ensure the optimal quality at all stages of biobanking, specific protocols are required and should be elaborated according to updated guidelines, recommendations, laws, and rules. This article illustrates the standard operating procedures, including protocols, troubleshooting, and quality controls, of a fully certified biobank in a referral Cancer Center. This model involves all clinical departments and research groups to support the dual mission of academic cancer centers, i.e. to provide high-quality care and high-quality research. All biobanking activities based on the type of biological specimens are detailed and the most tricky methodological aspects are discussed, from patients’ informed consent to specimen management.

Circulating endothelial cells as a novel marker of angiogenesis

Measurement of tumor angiogenesis to predict and/or to assess the efficacy of antiangiogenic therapies is mainly based on the evaluation of microvessel density (MVD). We developed a novel flow cytometry procedure to measure circulating endothelial cells (CECs) and circulating endothelial cells progenitors (CECPs) in either preclinical and clinical studies. Preclinical studies were performed on an animal model of human lymphoma. A trend toward higher CECs values was observed on day 7 and 14 after transplant, and differences vs controls were highly significant on day 21 (p = 0.0061). A strong correlation was found between CECs and tumor volume (r = 0.942, p = 0.004) and between CECs and tumor-generated VEGF (r = 0.669, p = 0.02). In mice given cyclophosphamide, most of circulating apoptotic cells were hematopoietic and not endothelial. Conversely, in mice given endostatin, all of the increase in apoptotic cells was in the endothelial cell compartment. In a parallel study, we looked for CECs in the peripheral blood of 20 healthy controls and 76 newly diagnosed cancer patients by means of four-color flow cytometry. In breast cancer (n = 46) and lymphoma (n = 30) patients, both resting and activated CECs were increased by 5 fold (P < 0.0008 vs control). CECs significantly correlated with plasma levels of VCAM-1 and VEGF. Resting and activated CECs were similar to healthy controls in 7 lymphoma patients achieving complete remission after chemotherapy, and activated CECs were found to decrease in 13 breast cancer patients evaluated before and 24h after quadrantectomy. In conclusion, our findings indicate a close relation between CEC increase and tumor progression, and support CECs evaluation as a clinically relevant, non invasive angiogenesis marker. Furthermore, this assay offers insight into anti-angiogenic activity of different drugs.

[Detection of mycobacterium tuberculosis DNA using nested polymerase chain reaction in lymph nodes with sarcoidosis, fixed in formalin and embedded in paraffin]

An involvement of mycobacteria in the pathogenesis of sarcoidosis has often been hypothesized, but not confirmed reproducibly. In this study we applied a nested Polymerase Chain Reaction (PCR) to the insertion sequence IS6110 for detection of Mycobacterium tuberculosis (MT) DNA in formalin-fixed and paraffin-embedded tissues from patients with sarcoidosis, with tuberculosis and atypical mycobacteriosis confirmed by culture, and from negative control samples. MT-DNA could be detected in 2/30 samples of sarcoidosis, in 10/10 tuberculoses, in 0/5 atypical mycobacterioses and in 0/10 negative controls. Nested PCR confirmed its high sensitivity and specificity in detecting MT-DNA on archival histopathological specimens. From our results we conclude that in the granulomatous lesions of sarcoidosis MT-DNA is only sporadically demonstrable and probably it doesn't play a role in the pathogenesis of this disease.